Association between cancer and dementia risk in the UK Biobank: evidence of diagnostic bias (original) (raw)

Abstract

Epidemiological studies have identified an inverse association between cancer and dementia. Underlying methodological biases have been postulated, yet no studies have systematically investigated the potential for each source of bias within a single dataset. We used the UK Biobank to compare estimates for the cancer-dementia association using different analytical specifications designed to sequentially address multiple sources of bias, including competing risk of death, selective survival, confounding bias, and diagnostic bias. We included 140,959 UK Biobank participants aged ≥ 55 without dementia before enrollment and with linked primary care data. We used cancer registry data to identify cancer cases prevalent before UK Biobank enrollment and incident cancer diagnosed after enrollment. We used Cox models to evaluate associations of prevalent and incident cancer with all-cause dementia, Alzheimer’s disease (AD), and vascular dementia. We used time-varying models to evaluate diagnostic bias. Over a median follow-up of 12.3 years, 3,310 dementia cases were diagnosed. All-site incident cancer was positively associated with all-cause dementia incidence (hazard ratio [HR] = 1.14, 95% CI: 1.02–1.29), but prevalent cancer was not (HR = 1.04, 95% CI: 0.92–1.17). Results were similar for vascular dementia. AD was not associated with prevalent or incident cancer. Dementia diagnosis was substantially elevated in the first year after cancer diagnosis (HR = 1.83, 95% CI: 1.42–2.36), after which the association attenuated to null, suggesting diagnostic bias. Following a cancer diagnosis, health care utilization or cognitive consequences of diagnosis or treatment may increase chance of receiving a dementia diagnosis, creating potential diagnostic bias in electronic health records-based studies.

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Funding

This work was supported by NIH/NIA RF1AG059872 (MMG), NIH/NIA K99AG073454 (SFA), and NIH/NIA K99AG075317 (EHL).

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
    Jingxuan Wang, Peter Buto, Sarah F. Ackley, Rebecca E. Graff, Scott C. Zimmerman, Stephen B. Asiimwe, Camilla Calmasini & M. Maria Glymour
  2. Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
    Lindsay C. Kobayashi
  3. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
    Eleanor Hayes-Larson & Elizabeth Rose Mayeda

Authors

  1. Jingxuan Wang
  2. Peter Buto
  3. Sarah F. Ackley
  4. Lindsay C. Kobayashi
  5. Rebecca E. Graff
  6. Scott C. Zimmerman
  7. Eleanor Hayes-Larson
  8. Elizabeth Rose Mayeda
  9. Stephen B. Asiimwe
  10. Camilla Calmasini
  11. M. Maria Glymour

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jingxuan Wang, Peter Buto and Sarah F. Ackley. The first draft of the manuscript was written by Jingxuan Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence toM. Maria Glymour.

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Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

The UK Biobank study was approved by the North West Multi-Centre Research Ethics Committee (Reference Number 16/NW/0274). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

As a part of the UK Biobank recruitment process, informed consent was obtained from all participants included in the study.

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Wang, J., Buto, P., Ackley, S.F. et al. Association between cancer and dementia risk in the UK Biobank: evidence of diagnostic bias.Eur J Epidemiol 38, 1069–1079 (2023). https://doi.org/10.1007/s10654-023-01036-x

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